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Adjusting Body Mass for Measurement Error with Invalid Validation Data

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  • Charles Courtemanche
  • Joshua C. Pinkston
  • Jay Stewart

Abstract

We propose a new method for using validation data to correct self-reported weight and height in surveys that do not measure respondents. The standard correction in prior research regresses actual measures on reported values using an external validation dataset, and then uses the estimated coefficients to predict actual measures in the primary dataset. This approach requires the strong assumption that the expectations of measured weight and height conditional on the reported values are the same in both datasets. In contrast, we use percentile ranks rather than levels of reported weight and height. Our approach requires the weaker assumption that the conditional expectations of actual measures are increasing in reported values in both samples. This makes our correction more robust to differences in measurement error across surveys as long as both surveys represent the same population. We examine three nationally representative datasets and find that misreporting appears to be sensitive to differences in survey context. When we compare predicted BMI distributions using the two validation approaches, we find that the standard correction is affected by differences in misreporting while our correction is not. Finally, we present several examples that demonstrate the potential importance of our correction for future econometric analyses and estimates of obesity rates.

Suggested Citation

  • Charles Courtemanche & Joshua C. Pinkston & Jay Stewart, 2014. "Adjusting Body Mass for Measurement Error with Invalid Validation Data," NBER Working Papers 19928, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:19928
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Courtemanche, Charles & Marton, James & Ukert, Benjamin & Yelowitz, Aaron & Zapata, Daniela, 2017. "Early Effects of the Affordable Care Act on Health Care Access, Risky Health Behaviors, and Self-Assessed Health," IZA Discussion Papers 10649, Institute for the Study of Labor (IZA).
    2. Rusty Tchernis & Lorenzo N. Almada, 2016. "Measuring Effects of SNAP on Obesity at the Intensive Margin," Working Papers 2016-019, Human Capital and Economic Opportunity Working Group.
    3. Lorenzo Almada & Ian McCarthy & Rusty Tchernis, 2016. "What Can We Learn about the Effects of Food Stamps on Obesity in the Presence of Misreporting?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 98(4), pages 997-1017.
    4. Huixia Wang & Chenggang Wang & Timothy J. Halliday, 2016. "Health and Health Inequality during the Great Recession: Evidence from the PSID," Working Papers 2016-14, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    5. Burke, Mary A. & Carman, Katherine G., 2017. "You can be too thin (but not too tall): Social desirability bias in self-reports of weight and height," Economics & Human Biology, Elsevier, vol. 27(PA), pages 198-222.
    6. Courtemanche, Charles & Tchernis, Rusty & Zhou, Xilin, 2017. "Parental Work Hours and Childhood Obesity: Evidence Using Instrumental Variables Related to Sibling School Eligibility," IZA Discussion Papers 10739, Institute for the Study of Labor (IZA).
    7. Buchmueller, Thomas C. & Johar, Meliyanni, 2015. "Obesity and health expenditures: Evidence from Australia," Economics & Human Biology, Elsevier, vol. 17(C), pages 42-58.
    8. Classen, Timothy J. & Thompson, Owen, 2016. "Genes and the intergenerational transmission of BMI and obesity," Economics & Human Biology, Elsevier, vol. 23(C), pages 121-133.
    9. Courtemanche, Charles & Pinkston, Joshua C. & Stewart, Jay, 2015. "Adjusting body mass for measurement error with invalid validation data," Economics & Human Biology, Elsevier, vol. 19(C), pages 275-293.
    10. repec:eee:joecag:v:9:y:2017:i:c:p:122-144 is not listed on IDEAS
    11. repec:eee:jhecon:v:57:y:2018:i:c:p:31-44 is not listed on IDEAS
    12. Buttet, Sebastien & Dolar, Veronika, 2015. "Toward a quantitative theory of food consumption choices and body weight," Economics & Human Biology, Elsevier, vol. 17(C), pages 143-156.
    13. Chenggang Wang & Huixia Wang & Timothy J. Halliday, 2017. "Health and Health Inequality during the Great Recession: Evidence from the PSID," Working Papers 201703, University of Hawaii at Manoa, Department of Economics.
    14. Courtemanche, Charles & Tchernis, Rusty & Ukert, Benjamin, 2018. "The effect of smoking on obesity: Evidence from a randomized trial," Journal of Health Economics, Elsevier, vol. 57(C), pages 31-44.
    15. Cawley, John & Maclean, Johanna Catherine & Hammer, Mette & Wintfeld, Neil, 2015. "Reporting error in weight and its implications for bias in economic models," Economics & Human Biology, Elsevier, vol. 19(C), pages 27-44.
    16. Barbaresco, Silvia & Courtemanche, Charles J. & Qi, Yanling, 2015. "Impacts of the Affordable Care Act dependent coverage provision on health-related outcomes of young adults," Journal of Health Economics, Elsevier, vol. 40(C), pages 54-68.
    17. Charles J. Courtemanche & Joshua C. Pinkston & Christopher J. Ruhm & George L. Wehby, 2016. "Can Changing Economic Factors Explain the Rise in Obesity?," Southern Economic Journal, Southern Economic Association, vol. 82(4), pages 1266-1310, April.
    18. repec:rre:publsh:v47:y:2017:i:3:p:309-329 is not listed on IDEAS
    19. Anthony M Yezer & Stephen J Popick, 2017. "Climate Preferences, Obesity, and Unobserved Heterogeneity in Cities," The Review of Regional Studies, Southern Regional Science Association, vol. 47(3), pages 309-329, Fall.
    20. Courtemanche, Charles & Marton, James & Ukert, Benjamin & Yelowitz, Aaron & Zapata, Daniela, 2018. "Effects of the Affordable Care Act on Health Behaviors after Three Years," IZA Discussion Papers 11468, Institute for the Study of Labor (IZA).
    21. repec:eee:ehbiol:v:26:y:2017:i:c:p:112-125 is not listed on IDEAS
    22. Johanna Catherine Maclean & Asia Sikora Kessler, 2015. "Reporting error in weight and height among the elderly: Implications and recommendations for estimating healthcare costs," DETU Working Papers 1501, Department of Economics, Temple University.

    More about this item

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • I1 - Health, Education, and Welfare - - Health

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